Clustering analysis is an important step towards getting insight into new data. Ensemble procedures have been designed in order to obtain improved partitions of a data set. Previous work in domain, mostly empirical, shows that accuracy and a limited diversity are mandatory features for successful ensemble construction. This paper presents a method which integrates unsupervised feature selection with ensemble clustering in order to deliver more accurate partitions. The efficiency of the method is studied on real data sets.
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